Discover Awesome MCP Servers

F5 MCP Server

Enables interaction with F5 devices through the iControl REST API to manage objects like virtual servers, pools, iRules, and profiles. It provides comprehensive tools for listing, creating, updating, and deleting F5 configurations via natural language interfaces.

ovice MCP Server

Integrates with the ovice Public API to manage virtual office workspaces, groups, and users. It enables Claude to send targeted notifications and retrieve organizational metadata such as space names and access URLs.

Aegis

Credential isolation proxy for AI agents. Injects API keys at the network boundary so your agent never sees the raw credential. Supports domain allowlists, agent auth, policy enforcement, and audit logging.

Rime MCP

Un servidor de Protocolo de Contexto de Modelo que permite a los modelos de IA generar y reproducir audio de texto a voz de alta calidad a través del sistema de audio nativo de tu dispositivo utilizando la API de síntesis de voz de Rime.

RefundYourSOL

Solana wallet cleanup, token trading on 12+ DEXes, and market data. Scan wallets for reclaimable SOL, close empty token accounts, burn dust tokens, buy/sell with Jito MEV protection. 7 tools for AI agents.

Weather MCP Server

Enables users to get current weather information for any city using the Open-Meteo API. Provides detailed meteorological data including temperature, precipitation, day/night status, and hourly forecasts through natural language queries.

PowerShell MCP Server

A Model Context Protocol server that enables AI assistants to execute PowerShell commands, retrieve system information, manage modules, and run scripts on Windows systems.

File Search Server

Enables intelligent file searching in local directories using natural language queries. Supports searching by file type, filename patterns, and content across multiple formats including PDF, Word, Excel, and text files with AI-powered relevance scoring.

Karakeep MCP Server

Okay, here's how you can search for and create bookmarks in Karakeep (Hoarder), translated into Spanish: **English:** **Searching:** 1. **Open Karakeep (Hoarder).** 2. **Use the Search Bar:** Look for the search bar, usually located at the top of the application window. 3. **Enter Your Search Term:** Type the word, phrase, or URL you're looking for in the search bar. 4. **Press Enter or Click the Search Icon:** This will initiate the search. 5. **Review the Results:** Karakeep will display a list of bookmarks that match your search term. **Creating Bookmarks:** 1. **Open Karakeep (Hoarder).** 2. **Navigate to the Page You Want to Bookmark:** Open the website or document you want to save. 3. **Use the Bookmark Function:** This might be a button, menu option, or keyboard shortcut. Look for something like "Add Bookmark," "Save Page," or a star icon. 4. **Edit Bookmark Details (Optional):** A dialog box might appear allowing you to: * **Change the Title:** Give the bookmark a descriptive name. * **Add Tags/Categories:** Organize your bookmarks by assigning them tags or categories. * **Choose a Folder:** Save the bookmark to a specific folder within Karakeep. 5. **Save the Bookmark:** Click "Save," "Add," or a similar button to finalize the bookmark creation. **Spanish:** **Búsqueda:** 1. **Abre Karakeep (Hoarder).** 2. **Usa la Barra de Búsqueda:** Busca la barra de búsqueda, que generalmente se encuentra en la parte superior de la ventana de la aplicación. 3. **Introduce tu Término de Búsqueda:** Escribe la palabra, frase o URL que estás buscando en la barra de búsqueda. 4. **Presiona Enter o Haz Clic en el Icono de Búsqueda:** Esto iniciará la búsqueda. 5. **Revisa los Resultados:** Karakeep mostrará una lista de marcadores que coincidan con tu término de búsqueda. **Creación de Marcadores:** 1. **Abre Karakeep (Hoarder).** 2. **Navega a la Página que Quieres Marcar:** Abre el sitio web o documento que deseas guardar. 3. **Usa la Función de Marcador:** Esto podría ser un botón, una opción de menú o un atajo de teclado. Busca algo como "Añadir Marcador", "Guardar Página" o un icono de estrella. 4. **Edita los Detalles del Marcador (Opcional):** Podría aparecer un cuadro de diálogo que te permita: * **Cambiar el Título:** Dale al marcador un nombre descriptivo. * **Añadir Etiquetas/Categorías:** Organiza tus marcadores asignándoles etiquetas o categorías. * **Elegir una Carpeta:** Guarda el marcador en una carpeta específica dentro de Karakeep. 5. **Guarda el Marcador:** Haz clic en "Guardar", "Añadir" o un botón similar para finalizar la creación del marcador. **Important Notes:** * The exact wording and appearance of the interface may vary slightly depending on the specific version of Karakeep (Hoarder) you are using. * If you are having trouble finding a specific feature, consult the Karakeep (Hoarder) documentation or help files. Let me know if you have any other questions!

tavily-mcp-python

Tavily MCP Server implementation that uses fastmcp and supports both sse and stdio transports. It also supports more up to date functionalities of Tavily.

AVS Document Search System

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

n8n-mcp

A Python-based MCP server adapted from the n8n_agent project that implements a note storage and summarization system. It enables users to create, retrieve, and summarize notes through the Model Context Protocol.

Monad NFT Launch Tool

An MCP server that helps users create NFT collections, deploy smart contracts to the Monad blockchain, and generate mint websites with Claude AI integration.

MCP Chat

A command-line interface application that enables interaction with LLMs through document retrieval, command-based prompts, and extensible tool integrations using the Model Control Protocol architecture.

GemForge-MCP

Professional Gemini API integration for Claude and MCP-compatible hosts with intelligent model selection and advanced file handling capabilities.

Azure AI Foundry MCP Server

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

RS.ge Waybill MCP Server

Enables natural language queries for Georgian tax system waybills through the RS.ge SOAP API. Supports waybill retrieval by date range, company TIN lookups, and access to tax system dictionaries.

Image Generator

I can't directly generate and return an image using Together.ai (or any other service) within this text-based environment. I am a language model, not an image generation tool. However, I can provide you with the information you need to do it yourself. Here's what you'd typically need to do: 1. **Access Together.ai's API:** You'll need to sign up for an account on Together.ai and obtain an API key. This key is how you authenticate your requests. 2. **Use their API documentation:** Together.ai will have documentation on how to use their image generation API. This documentation will tell you: * **The endpoint (URL) to send your request to.** * **The required parameters:** This will almost certainly include a text prompt (the description of the image you want to generate). It might also include parameters for image size, style, number of images, etc. * **The format of the request (usually JSON).** * **The format of the response:** This will likely include a URL or data representing the generated image. 3. **Write code to make the API request:** You'll need to use a programming language (like Python) and a library that can make HTTP requests (like `requests` in Python) to send the request to Together.ai's API. 4. **Process the response:** Once you get the response from Together.ai, you'll need to parse it to extract the image data or URL. 5. **Display or save the image:** Finally, you can display the image in your application or save it to a file. **Example (Conceptual Python Code - Requires Together.ai API Key and Installation of `requests`):** ```python import requests import json # Replace with your actual Together.ai API key API_KEY = "YOUR_TOGETHERAI_API_KEY" def generate_image(prompt): """Generates an image using Together.ai based on the given prompt.""" url = "THE_TOGETHERAI_IMAGE_GENERATION_ENDPOINT" # Replace with the actual endpoint headers = { "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json" } data = { "prompt": prompt, "width": 512, # Example: Image width "height": 512 # Example: Image height # Add other parameters as needed based on Together.ai's documentation } try: response = requests.post(url, headers=headers, data=json.dumps(data)) response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx) response_json = response.json() # Assuming the response contains a URL to the image image_url = response_json.get("image_url") # Adjust based on the actual response structure if image_url: print(f"Image URL: {image_url}") # You can then download the image using requests.get(image_url) # and save it to a file. Or display it in a GUI. return image_url # Or return the image data itself if that's what the API provides else: print("Error: Image URL not found in the response.") return None except requests.exceptions.RequestException as e: print(f"Error making API request: {e}") return None except json.JSONDecodeError: print("Error: Could not decode JSON response.") return None # Example usage: prompt = "A futuristic cityscape at sunset" image_url = generate_image(prompt) if image_url: print("Image generated successfully!") # Further processing of the image (download, display, etc.) else: print("Image generation failed.") ``` **Important Considerations:** * **API Documentation is Key:** The most important thing is to carefully read and understand Together.ai's API documentation. The code above is just a general example; you'll need to adapt it to their specific requirements. * **Error Handling:** The example includes basic error handling, but you should add more robust error handling to catch potential issues like network problems, invalid API keys, or incorrect parameters. * **Rate Limits:** Be aware of Together.ai's rate limits (how many requests you can make per minute/hour). You might need to implement logic to handle rate limiting. * **Cost:** Using image generation APIs often incurs costs. Understand Together.ai's pricing model before you start using the API extensively. **Translation to Spanish (of the explanation, not the code):** No puedo generar y devolver directamente una imagen usando Together.ai (o cualquier otro servicio) dentro de este entorno basado en texto. Soy un modelo de lenguaje, no una herramienta de generación de imágenes. Sin embargo, puedo proporcionarte la información que necesitas para hacerlo tú mismo. Esto es lo que normalmente necesitarías hacer: 1. **Acceder a la API de Together.ai:** Necesitarás registrarte para obtener una cuenta en Together.ai y obtener una clave API. Esta clave es cómo autenticas tus solicitudes. 2. **Usar su documentación de la API:** Together.ai tendrá documentación sobre cómo usar su API de generación de imágenes. Esta documentación te dirá: * **El endpoint (URL) al que enviar tu solicitud.** * **Los parámetros requeridos:** Esto casi seguro que incluirá un prompt de texto (la descripción de la imagen que quieres generar). También podría incluir parámetros para el tamaño de la imagen, el estilo, el número de imágenes, etc. * **El formato de la solicitud (normalmente JSON).** * **El formato de la respuesta:** Esto probablemente incluirá una URL o datos que representen la imagen generada. 3. **Escribir código para hacer la solicitud a la API:** Necesitarás usar un lenguaje de programación (como Python) y una biblioteca que pueda hacer solicitudes HTTP (como `requests` en Python) para enviar la solicitud a la API de Together.ai. 4. **Procesar la respuesta:** Una vez que obtengas la respuesta de Together.ai, necesitarás analizarla para extraer los datos de la imagen o la URL. 5. **Mostrar o guardar la imagen:** Finalmente, puedes mostrar la imagen en tu aplicación o guardarla en un archivo. **Consideraciones importantes:** * **La documentación de la API es clave:** Lo más importante es leer y comprender cuidadosamente la documentación de la API de Together.ai. El código anterior es solo un ejemplo general; necesitarás adaptarlo a sus requisitos específicos. * **Manejo de errores:** El ejemplo incluye un manejo básico de errores, pero debes agregar un manejo de errores más robusto para detectar posibles problemas como problemas de red, claves API no válidas o parámetros incorrectos. * **Límites de velocidad:** Ten en cuenta los límites de velocidad de Together.ai (cuántas solicitudes puedes hacer por minuto/hora). Es posible que debas implementar lógica para manejar la limitación de velocidad. * **Costo:** El uso de las API de generación de imágenes a menudo incurre en costos. Comprende el modelo de precios de Together.ai antes de comenzar a usar la API extensivamente.

Postgres MCP Pro

An open-source MCP server that provides AI agents with advanced PostgreSQL capabilities including index tuning, query plan optimization, and comprehensive database health analysis. It supports safe SQL execution through configurable access modes and offers both stdio and SSE transport options for various development environments.

GitLab MCP Server

Connects AI assistants to GitLab projects, enabling natural language queries for merge requests, code reviews, test results, pipelines, and discussions. Supports viewing MR details, responding to comments, and analyzing CI/CD job logs.

MCP Refchecker

MCP server for verifying academic citations via Semantic Scholar, OpenAlex, and CrossRef.

SSH Linux Control

Enables remote Linux VM management via SSH with automatic safety checks for destructive commands, supporting password authentication and sudo operations.

mcp-crew

Agent Personas for Claude. 10 tools, 8 personas, 3 workflows. Zero API cost.

gmail-mcp

A minimal MCP server that enables Claude to search, read, and manage Gmail messages and threads using official Google API libraries. It supports actions like sending emails, creating drafts, replying to threads, and managing labels through secure OAuth2 authentication.

Wireshark MCP Server

Provides AI assistants with direct access to Wireshark network analysis capabilities, enabling AI-powered network troubleshooting, packet analysis, and network monitoring through a secure interface.

RISEN Prompt Engineering MCP Tool

A Model Context Protocol server that helps users create, validate, and optimize AI prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).

Weather MCP Server

Provides current weather data and city comparisons for any location with support for metric/imperial units and optional forecasts.

Skippr Extension MCP Server

Enables AI coding agents to discover, understand, and fix product issues (UX inconsistencies, accessibility violations, quality problems) identified by Skippr's browser extension through natural language commands.